IMPORTING LIBRARIES¶
In [ ]:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
# Import BaseSettings from pydantic_settings
%pip install ydata_profiling
from ydata_profiling import ProfileReport
Collecting ydata_profiling
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Collecting seaborn<0.13,>=0.10.1 (from ydata_profiling)
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Collecting statsmodels<1,>=0.13.2 (from ydata_profiling)
Using cached statsmodels-0.14.1-cp312-cp312-win_amd64.whl.metadata (9.8 kB)
Collecting typeguard<5,>=4.1.2 (from ydata_profiling)
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Collecting wordcloud>=1.9.1 (from ydata_profiling)
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Collecting dacite>=1.8 (from ydata_profiling)
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Using cached patsy-0.5.6-py2.py3-none-any.whl (233 kB)
Installing collected packages: typeguard, scipy, patsy, llvmlite, dacite, numba, wordcloud, visions, statsmodels, seaborn, ydata_profiling
Attempting uninstall: scipy
Found existing installation: scipy 1.11.4
Uninstalling scipy-1.11.4:
Successfully uninstalled scipy-1.11.4
Attempting uninstall: visions
Found existing installation: visions 0.7.6
Uninstalling visions-0.7.6:
Successfully uninstalled visions-0.7.6
Attempting uninstall: seaborn
Found existing installation: seaborn 0.12.2
Uninstalling seaborn-0.12.2:
Successfully uninstalled seaborn-0.12.2
Successfully installed dacite-1.8.1 llvmlite-0.42.0 numba-0.59.1 patsy-0.5.6 scipy-1.11.4 seaborn-0.12.2 statsmodels-0.14.1 typeguard-4.1.5 visions-0.7.6 wordcloud-1.9.3 ydata_profiling-4.7.0
Note: you may need to restart the kernel to use updated packages.
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. pandas-profiling 3.2.0 requires visions[type_image_path]==0.7.4, but you have visions 0.7.6 which is incompatible. scikit-learn 1.4.1.post1 requires joblib>=1.2.0, but you have joblib 1.1.1 which is incompatible.
IMPORTING DATASETS¶
In [ ]:
# Reading The Data By Pandas
data=pd.read_csv("D:\MAIN DRIVE\VSCODE\Regression-Project\data.csv")
# Creating 'X' Matrix For Independent Features In The Dataset
X=data.iloc[:,:-1].values
# Creating 'Y' Matrix For Independent Features In The Dataset
Y=data.iloc[:,-1].values
<>:2: SyntaxWarning: invalid escape sequence '\M'
<>:2: SyntaxWarning: invalid escape sequence '\M'
C:\Users\surya\AppData\Local\Temp\ipykernel_22704\2686243820.py:2: SyntaxWarning: invalid escape sequence '\M'
data=pd.read_csv("D:\MAIN DRIVE\VSCODE\Regression-Project\data.csv")
GENRATING PROFILE USING PANDAS PROFILING¶
In [ ]:
profile=ProfileReport(data)
profile.to_notebook_iframe()
Summarize dataset: 0%| | 0/5 [00:00<?, ?it/s]
Generate report structure: 0%| | 0/1 [00:00<?, ?it/s]
Render HTML: 0%| | 0/1 [00:00<?, ?it/s]
<!-- Attribute Information:¶
1. CRIM per capita crime rate by town
2. ZN proportion of residential land zoned for lots over 25,000 sq.ft.
3. INDUS proportion of non-retail business acres per town
4. CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
5. NOX nitric oxides concentration (parts per 10 million)
6. RM average number of rooms per dwelling
7. AGE proportion of owner-occupied units built prior to 1940
8. DIS weighted distances to five Boston employment centres
9. RAD index of accessibility to radial highways
10. TAX full-value property-tax rate per $10,000
11. PTRATIO pupil-teacher ratio by town
12. B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
13. LSTAT % lower status of the population
14. MEDV Median value of owner-occupied homes in $1000's --> ¶
In [ ]:
data
Out[ ]:
| CRIM | ZN | INDUS | CHAS | NOX | RM | AGE | DIS | RAD | TAX | PTRATIO | B | LSTAT | MEDV | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.00632 | 18.0 | 2.31 | 0 | 0.538 | 6.575 | 65.2 | 4.0900 | 1 | 296 | 15.3 | 396.90 | 4.98 | 24.0 |
| 1 | 0.02731 | 0.0 | 7.07 | 0 | 0.469 | 6.421 | 78.9 | 4.9671 | 2 | 242 | 17.8 | 396.90 | 9.14 | 21.6 |
| 2 | 0.02729 | 0.0 | 7.07 | 0 | 0.469 | 7.185 | 61.1 | 4.9671 | 2 | 242 | 17.8 | 392.83 | 4.03 | 34.7 |
| 3 | 0.03237 | 0.0 | 2.18 | 0 | 0.458 | 6.998 | 45.8 | 6.0622 | 3 | 222 | 18.7 | 394.63 | 2.94 | 33.4 |
| 4 | 0.06905 | 0.0 | 2.18 | 0 | 0.458 | 7.147 | 54.2 | 6.0622 | 3 | 222 | 18.7 | 396.90 | 5.33 | 36.2 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 501 | 0.06263 | 0.0 | 11.93 | 0 | 0.573 | 6.593 | 69.1 | 2.4786 | 1 | 273 | 21.0 | 391.99 | 9.67 | 22.4 |
| 502 | 0.04527 | 0.0 | 11.93 | 0 | 0.573 | 6.120 | 76.7 | 2.2875 | 1 | 273 | 21.0 | 396.90 | 9.08 | 20.6 |
| 503 | 0.06076 | 0.0 | 11.93 | 0 | 0.573 | 6.976 | 91.0 | 2.1675 | 1 | 273 | 21.0 | 396.90 | 5.64 | 23.9 |
| 504 | 0.10959 | 0.0 | 11.93 | 0 | 0.573 | 6.794 | 89.3 | 2.3889 | 1 | 273 | 21.0 | 393.45 | 6.48 | 22.0 |
| 505 | 0.04741 | 0.0 | 11.93 | 0 | 0.573 | 6.030 | 80.8 | 2.5050 | 1 | 273 | 21.0 | 396.90 | 7.88 | 11.9 |
506 rows × 14 columns
In [ ]:
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 506 entries, 0 to 505 Data columns (total 14 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 CRIM 506 non-null float64 1 ZN 506 non-null float64 2 INDUS 506 non-null float64 3 CHAS 506 non-null int64 4 NOX 506 non-null float64 5 RM 501 non-null float64 6 AGE 506 non-null float64 7 DIS 506 non-null float64 8 RAD 506 non-null int64 9 TAX 506 non-null int64 10 PTRATIO 506 non-null float64 11 B 506 non-null float64 12 LSTAT 506 non-null float64 13 MEDV 506 non-null float64 dtypes: float64(11), int64(3) memory usage: 55.5 KB
In [ ]:
# "CHAR" IS A UNBALANCED COLUMN <BIASED>
data['CHAS'].value_counts()
Out[ ]:
CHAS 0 471 1 35 Name: count, dtype: int64
In [ ]:
data.describe()
Out[ ]:
| CRIM | ZN | INDUS | CHAS | NOX | RM | AGE | DIS | RAD | TAX | PTRATIO | B | LSTAT | MEDV | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 501.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 |
| mean | 3.613524 | 11.363636 | 11.136779 | 0.069170 | 0.554695 | 6.284341 | 68.574901 | 3.795043 | 9.549407 | 408.237154 | 18.455534 | 356.674032 | 12.653063 | 22.532806 |
| std | 8.601545 | 23.322453 | 6.860353 | 0.253994 | 0.115878 | 0.705587 | 28.148861 | 2.105710 | 8.707259 | 168.537116 | 2.164946 | 91.294864 | 7.141062 | 9.197104 |
| min | 0.006320 | 0.000000 | 0.460000 | 0.000000 | 0.385000 | 3.561000 | 2.900000 | 1.129600 | 1.000000 | 187.000000 | 12.600000 | 0.320000 | 1.730000 | 5.000000 |
| 25% | 0.082045 | 0.000000 | 5.190000 | 0.000000 | 0.449000 | 5.884000 | 45.025000 | 2.100175 | 4.000000 | 279.000000 | 17.400000 | 375.377500 | 6.950000 | 17.025000 |
| 50% | 0.256510 | 0.000000 | 9.690000 | 0.000000 | 0.538000 | 6.208000 | 77.500000 | 3.207450 | 5.000000 | 330.000000 | 19.050000 | 391.440000 | 11.360000 | 21.200000 |
| 75% | 3.677083 | 12.500000 | 18.100000 | 0.000000 | 0.624000 | 6.625000 | 94.075000 | 5.188425 | 24.000000 | 666.000000 | 20.200000 | 396.225000 | 16.955000 | 25.000000 |
| max | 88.976200 | 100.000000 | 27.740000 | 1.000000 | 0.871000 | 8.780000 | 100.000000 | 12.126500 | 24.000000 | 711.000000 | 22.000000 | 396.900000 | 37.970000 | 50.000000 |
VISUALIZING THE DATASET¶
In [ ]:
#For plotting histogram
print("HISTOGRAM")
%matplotlib inline
data.hist(bins=50, figsize=(20, 15))
HISTOGRAM
Out[ ]:
array([[<Axes: title={'center': 'CRIM'}>, <Axes: title={'center': 'ZN'}>,
<Axes: title={'center': 'INDUS'}>,
<Axes: title={'center': 'CHAS'}>],
[<Axes: title={'center': 'NOX'}>, <Axes: title={'center': 'RM'}>,
<Axes: title={'center': 'AGE'}>, <Axes: title={'center': 'DIS'}>],
[<Axes: title={'center': 'RAD'}>, <Axes: title={'center': 'TAX'}>,
<Axes: title={'center': 'PTRATIO'}>,
<Axes: title={'center': 'B'}>],
[<Axes: title={'center': 'LSTAT'}>,
<Axes: title={'center': 'MEDV'}>, <Axes: >, <Axes: >]],
dtype=object)
In [ ]:
print("COLORED - BOXPLOT")
sns.boxplot(data=data)
COLORED - BOXPLOT
Out[ ]:
<Axes: >
In [ ]:
print("PAIR - PLOT OF ALL FEATURES WITH LABEL")
sns.pairplot(data)
PAIR - PLOT OF ALL FEATURES WITH LABEL
Out[ ]:
<seaborn.axisgrid.PairGrid at 0x1c9800d6ff0>
In [ ]:
print("HEATMAP")
sns.heatmap(data.corr(), annot=True)
HEATMAP
Out[ ]:
<Axes: >
In [ ]:
print("Density - Plot")
sns.kdeplot(data, fill=True)
Density - Plot
Out[ ]:
<Axes: ylabel='Density'>
INDEPENDENT - FEATURES & DEPENDENT - LABEL¶
In [ ]:
print(F"Matrix For Independent Features In The Dataset -> \n{X}")
Matrix For Independent Features In The Dataset -> [[6.3200e-03 1.8000e+01 2.3100e+00 ... 1.5300e+01 3.9690e+02 4.9800e+00] [2.7310e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9690e+02 9.1400e+00] [2.7290e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9283e+02 4.0300e+00] ... [6.0760e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 5.6400e+00] [1.0959e-01 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9345e+02 6.4800e+00] [4.7410e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 7.8800e+00]]
In [ ]:
print(F"Matrix For Dependent Features In The Dataset -> MEVD \n{Y.reshape(len(Y),1)}")
Matrix For Dependent Features In The Dataset -> MEVD [[24. ] [21.6] [34.7] [33.4] [36.2] [28.7] [22.9] [27.1] [16.5] [18.9] [15. ] [18.9] [21.7] [20.4] [18.2] [19.9] [23.1] [17.5] [20.2] [18.2] [13.6] [19.6] [15.2] [14.5] [15.6] [13.9] [16.6] [14.8] [18.4] [21. ] [12.7] [14.5] [13.2] [13.1] [13.5] [18.9] [20. ] [21. ] [24.7] [30.8] [34.9] [26.6] [25.3] [24.7] [21.2] [19.3] [20. ] [16.6] [14.4] [19.4] [19.7] [20.5] [25. ] [23.4] [18.9] [35.4] [24.7] [31.6] [23.3] [19.6] [18.7] [16. ] [22.2] [25. ] [33. ] [23.5] [19.4] [22. ] [17.4] [20.9] [24.2] [21.7] [22.8] [23.4] [24.1] [21.4] [20. ] [20.8] [21.2] [20.3] [28. ] [23.9] [24.8] [22.9] [23.9] [26.6] [22.5] [22.2] [23.6] [28.7] [22.6] [22. ] [22.9] [25. ] [20.6] [28.4] [21.4] [38.7] [43.8] [33.2] [27.5] [26.5] [18.6] [19.3] [20.1] [19.5] [19.5] [20.4] [19.8] [19.4] [21.7] [22.8] [18.8] [18.7] [18.5] [18.3] [21.2] [19.2] [20.4] [19.3] [22. ] [20.3] [20.5] [17.3] [18.8] [21.4] [15.7] [16.2] [18. ] [14.3] [19.2] [19.6] [23. ] [18.4] [15.6] [18.1] [17.4] [17.1] [13.3] [17.8] [14. ] [14.4] [13.4] [15.6] [11.8] [13.8] [15.6] [14.6] [17.8] [15.4] [21.5] [19.6] [15.3] [19.4] [17. ] [15.6] [13.1] [41.3] [24.3] [23.3] [27. ] [50. ] [50. ] [50. ] [22.7] [25. ] [50. ] [23.8] [23.8] [22.3] [17.4] [19.1] [23.1] [23.6] [22.6] [29.4] [23.2] [24.6] [29.9] [37.2] [39.8] [36.2] [37.9] [32.5] [26.4] [29.6] [50. ] [32. ] [29.8] [34.9] [37. ] [30.5] [36.4] [31.1] [29.1] [50. ] [33.3] [30.3] [34.6] [34.9] [32.9] [24.1] [42.3] [48.5] [50. ] [22.6] [24.4] [22.5] [24.4] [20. ] [21.7] [19.3] [22.4] [28.1] [23.7] [25. ] [23.3] [28.7] [21.5] [23. ] [26.7] [21.7] [27.5] [30.1] [44.8] [50. ] [37.6] [31.6] [46.7] [31.5] [24.3] [31.7] [41.7] [48.3] [29. ] [24. ] [25.1] [31.5] [23.7] [23.3] [22. ] [20.1] [22.2] [23.7] [17.6] [18.5] [24.3] [20.5] [24.5] [26.2] [24.4] [24.8] [29.6] [42.8] [21.9] [20.9] [44. ] [50. ] [36. ] [30.1] [33.8] [43.1] [48.8] [31. ] [36.5] [22.8] [30.7] [50. ] [43.5] [20.7] [21.1] [25.2] [24.4] [35.2] [32.4] [32. ] [33.2] [33.1] [29.1] [35.1] [45.4] [35.4] [46. ] [50. ] [32.2] [22. ] [20.1] [23.2] [22.3] [24.8] [28.5] [37.3] [27.9] [23.9] [21.7] [28.6] [27.1] [20.3] [22.5] [29. ] [24.8] [22. ] [26.4] [33.1] [36.1] [28.4] [33.4] [28.2] [22.8] [20.3] [16.1] [22.1] [19.4] [21.6] [23.8] [16.2] [17.8] [19.8] [23.1] [21. ] [23.8] [23.1] [20.4] [18.5] [25. ] [24.6] [23. ] [22.2] [19.3] [22.6] [19.8] [17.1] [19.4] [22.2] [20.7] [21.1] [19.5] [18.5] [20.6] [19. ] [18.7] [32.7] [16.5] [23.9] [31.2] [17.5] [17.2] [23.1] [24.5] [26.6] [22.9] [24.1] [18.6] [30.1] [18.2] [20.6] [17.8] [21.7] [22.7] [22.6] [25. ] [19.9] [20.8] [16.8] [21.9] [27.5] [21.9] [23.1] [50. ] [50. ] [50. ] [50. ] [50. ] [13.8] [13.8] [15. ] [13.9] [13.3] [13.1] [10.2] [10.4] [10.9] [11.3] [12.3] [ 8.8] [ 7.2] [10.5] [ 7.4] [10.2] [11.5] [15.1] [23.2] [ 9.7] [13.8] [12.7] [13.1] [12.5] [ 8.5] [ 5. ] [ 6.3] [ 5.6] [ 7.2] [12.1] [ 8.3] [ 8.5] [ 5. ] [11.9] [27.9] [17.2] [27.5] [15. ] [17.2] [17.9] [16.3] [ 7. ] [ 7.2] [ 7.5] [10.4] [ 8.8] [ 8.4] [16.7] [14.2] [20.8] [13.4] [11.7] [ 8.3] [10.2] [10.9] [11. ] [ 9.5] [14.5] [14.1] [16.1] [14.3] [11.7] [13.4] [ 9.6] [ 8.7] [ 8.4] [12.8] [10.5] [17.1] [18.4] [15.4] [10.8] [11.8] [14.9] [12.6] [14.1] [13. ] [13.4] [15.2] [16.1] [17.8] [14.9] [14.1] [12.7] [13.5] [14.9] [20. ] [16.4] [17.7] [19.5] [20.2] [21.4] [19.9] [19. ] [19.1] [19.1] [20.1] [19.9] [19.6] [23.2] [29.8] [13.8] [13.3] [16.7] [12. ] [14.6] [21.4] [23. ] [23.7] [25. ] [21.8] [20.6] [21.2] [19.1] [20.6] [15.2] [ 7. ] [ 8.1] [13.6] [20.1] [21.8] [24.5] [23.1] [19.7] [18.3] [21.2] [17.5] [16.8] [22.4] [20.6] [23.9] [22. ] [11.9]]
VALIDATING THE MISSING VALUES IN THE DATASET¶
In [ ]:
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
X[:, :14] = imputer.fit_transform(X[:, :14])
In [ ]:
print(F"AFTER VALIDATING THE MISSING VALUES IN 'x' MATRIX : \n{X} ")
AFTER VALIDATING THE MISSING VALUES IN 'x' MATRIX : [[6.3200e-03 1.8000e+01 2.3100e+00 ... 1.5300e+01 3.9690e+02 4.9800e+00] [2.7310e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9690e+02 9.1400e+00] [2.7290e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9283e+02 4.0300e+00] ... [6.0760e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 5.6400e+00] [1.0959e-01 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9345e+02 6.4800e+00] [4.7410e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 7.8800e+00]]
< NO ENCODNG IS NEEDED >¶
TRAIN AND TEST SPLIT¶
In [ ]:
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)
In [ ]:
print(F"The Independent Training Set :{X_train.shape} \n{X_train}") # Printing The Independent Training Set
The Independent Training Set :(404, 13) [[3.5809e-01 0.0000e+00 6.2000e+00 ... 1.7400e+01 3.9170e+02 9.7100e+00] [1.5876e-01 0.0000e+00 1.0810e+01 ... 1.9200e+01 3.7694e+02 9.8800e+00] [1.1329e-01 3.0000e+01 4.9300e+00 ... 1.6600e+01 3.9125e+02 1.1380e+01] ... [1.5098e-01 0.0000e+00 1.0010e+01 ... 1.7800e+01 3.9451e+02 1.0300e+01] [2.2927e-01 0.0000e+00 6.9100e+00 ... 1.7900e+01 3.9274e+02 1.8800e+01] [1.3914e-01 0.0000e+00 4.0500e+00 ... 1.6600e+01 3.9690e+02 1.4690e+01]]
In [ ]:
print(F"The Dependent Training Set : {Y_train.shape} \n{Y_train}") # Printing The Dependent Training Set
The Dependent Training Set : (404,) [26.7 21.7 22. 22.9 10.4 21.9 20.6 26.4 41.3 17.2 27.1 20.4 16.5 24.4 8.4 23. 9.7 50. 30.5 12.3 19.4 21.2 20.3 18.8 33.4 18.5 19.6 33.2 13.1 7.5 13.6 17.4 8.4 35.4 24. 13.4 26.2 7.2 13.1 24.5 37.2 25. 24.1 16.6 32.9 36.2 11. 7.2 22.8 28.7 14.4 24.4 18.1 22.5 20.5 15.2 17.4 13.6 8.7 18.2 35.4 31.7 33. 22.2 20.4 23.9 25. 12.7 29.1 12. 17.7 27. 20.6 10.2 17.5 19.7 29.8 20.5 14.9 10.9 19.5 22.7 19.5 24.6 25. 24.5 50. 14.3 11.8 31. 28.7 16.2 43.5 25. 22. 19.9 22.1 46. 22.9 20.2 43.1 34.6 13.8 24.3 21.5 24.4 21.2 23.8 26.6 25.1 9.6 19.4 19.4 9.5 14. 26.5 13.8 34.7 16.3 21.7 17.5 15.6 20.9 21.7 12.7 18.5 23.7 19.3 12.7 21.6 23.2 29.6 21.2 23.8 17.1 22. 36.5 18.8 21.9 23.1 20.2 17.4 37. 24.1 36.2 15.7 32.2 13.5 17.9 13.3 11.7 41.7 18.4 13.1 25. 21.2 16. 34.9 25.2 24.8 21.5 23.4 18.9 10.8 21. 27.5 17.5 13.5 28.7 14.8 19.1 28.6 13.1 19. 11.3 13.3 22.4 20.1 18.2 22.9 20.6 25. 12.8 34.9 23.7 50. 29. 30.1 22. 15.6 23.3 30.1 14.3 22.8 50. 20.8 6.3 34.9 32.4 19.9 20.3 17.8 23.1 20.4 23.2 7. 16.8 46.7 50. 22.9 23.9 21.4 21.7 15.4 15.3 23.1 23.9 19.4 11.9 17.8 31.5 33.8 20.8 19.8 22.4 5. 24.5 19.4 15.1 18.2 19.3 27.1 20.7 37.6 11.7 33.4 30.1 21.4 45.4 20.1 20.8 26.4 10.4 21.8 32. 21.7 18.4 37.9 17.8 28. 28.2 36. 18.9 15. 22.5 30.7 20. 19.1 23.3 26.6 21.1 19.7 20. 12.1 7.2 14.2 17.3 27.5 22.2 10.9 19.2 32. 14.5 24.7 12.6 24. 24.1 50. 16.1 43.8 26.6 36.1 21.8 29.9 50. 44. 20.6 19.6 28.4 19.1 22.3 20.9 28.4 14.4 32.7 13.8 8.5 22.5 35.1 31.6 17.8 15.6 20.7 39.8 17.8 19.6 14.9 22. 48.8 25. 48.5 23.9 20.3 15.2 10.5 19. 16.4 8.8 22. 24.8 50. 19.3 22.7 37.3 31.6 8.3 23.1 50. 13.9 16.1 25.3 19.5 10.2 19.9 35.2 13.4 24.7 11.5 23.4 16.7 15.4 18. 28.5 18.4 32.5 50. 50. 19.6 17.6 42.3 14.5 13.2 16.2 29.6 16.7 13. 22.3 13.4 5. 19.5 14.6 22. 8.1 24.6 33.2 20.6 14.1 14.1 21.1 30.3 23.7 21.4 18.3 8.5 22.8 22.8 22.2 13.9 25. 18.5 7. 22.6 20.1 30.8 31.1 23.8 12.5 23.6 23.2 24.2 22.2 27.9 22.2 33.1 19.3 18.9 22.6 50. 24.8 18.5 36.4 19.2 16.6 23.1]
In [ ]:
print(F"The Independent Testing Set :{X_test.shape} \n{X_test}") # Printing The Independent Testing Set
The Independent Testing Set :(102, 13) [[6.7240e-02 0.0000e+00 3.2400e+00 ... 1.6900e+01 3.7521e+02 7.3400e+00] [9.2323e+00 0.0000e+00 1.8100e+01 ... 2.0200e+01 3.6615e+02 9.5300e+00] [1.1425e-01 0.0000e+00 1.3890e+01 ... 1.6400e+01 3.9374e+02 1.0500e+01] ... [1.4932e-01 2.5000e+01 5.1300e+00 ... 1.9700e+01 3.9511e+02 1.3150e+01] [1.4052e-01 0.0000e+00 1.0590e+01 ... 1.8600e+01 3.8581e+02 9.3800e+00] [1.2802e-01 0.0000e+00 8.5600e+00 ... 2.0900e+01 3.9524e+02 1.2270e+01]]
In [ ]:
print(F"The Dependent Testing Set : {Y_test.shape} \n{Y_test}") # Printing The Independent Testing Set
The Dependent Testing Set : (102,) [22.6 50. 23. 8.3 21.2 19.9 20.6 18.7 16.1 18.6 8.8 17.2 14.9 10.5 50. 29. 23. 33.3 29.4 21. 23.8 19.1 20.4 29.1 19.3 23.1 19.6 19.4 38.7 18.7 14.6 20. 20.5 20.1 23.6 16.8 5.6 50. 14.5 13.3 23.9 20. 19.8 13.8 16.5 21.6 20.3 17. 11.8 27.5 15.6 23.1 24.3 42.8 15.6 21.7 17.1 17.2 15. 21.7 18.6 21. 33.1 31.5 20.1 29.8 15.2 15. 27.5 22.6 20. 21.4 23.5 31.2 23.7 7.4 48.3 24.4 22.6 18.3 23.3 17.1 27.9 44.8 50. 23. 21.4 10.2 23.3 23.2 18.9 13.4 21.9 24.8 11.9 24.3 13.8 24.7 14.1 18.7 28.1 19.8]
Resolved The Unbalanced Column 'CHAS'¶
In [ ]:
# Convert it to a pandas Series
X_train_series = pd.DataFrame(X_train)
# Now you can use value_counts() on the Series
counts = X_train_series[3].value_counts()
print(counts)
3 0.0 376 1.0 28 Name: count, dtype: int64
In [ ]:
# Convert it to a pandas Series
X_test_series = pd.DataFrame(X_test)
# Now you can use value_counts() on the Series
counts = X_test_series[3].value_counts()
print(counts)
3 0.0 95 1.0 7 Name: count, dtype: int64
In [ ]:
print(F"Ratio Of The CHAS IN X_train : {95//7}")
Ratio Of The CHAS IN X_train : 13
In [ ]:
print(F"Ratio Of The CHAS IN X_train : {376//28}")
Ratio Of The CHAS IN X_train : 13
The "CHAS" Is Been Resolved And It Has been Splited Evenly In Both 'X_train' & 'X_test' Respectively ABOVE¶
FEATURE SCALING¶
In [ ]:
from sklearn.preprocessing import StandardScaler
FS=StandardScaler()
X_FS= FS.fit_transform(X_train)
Y_FS = FS.fit_transform(Y_train.reshape(len(Y_train),-1))
In [ ]:
print(F"Featured Scaled Values of X_train :{X_FS.shape} \n{X_FS}")
Featured Scaled Values of X_train :(404, 13) [[-0.37257438 -0.49960763 -0.70492455 ... -0.48463784 0.3716906 -0.41100022] [-0.39709866 -0.49960763 -0.04487755 ... 0.33649132 0.20501196 -0.38768057] [-0.402693 0.77116771 -0.88675963 ... -0.84958414 0.36660893 -0.18191902] ... [-0.39805586 -0.49960763 -0.15941933 ... -0.30216469 0.40342278 -0.33006734] [-0.38842357 -0.49960763 -0.60326872 ... -0.25654641 0.38343489 0.8359148 ] [-0.39951258 -0.49960763 -1.01275558 ... -0.84958414 0.43041207 0.27212814]]
In [ ]:
print(F"Featured Scaled Values of Y_train :{Y_FS.shape} \n{Y_FS}")
Featured Scaled Values of Y_train :(404, 1) [[ 4.43044345e-01] [-9.88238901e-02] [-6.63117960e-02] [ 3.12244864e-02] [-1.32344610e+00] [-7.71491607e-02] [-2.18034902e-01] [ 4.10532251e-01] [ 2.02529959e+00] [-5.86505302e-01] [ 4.86393804e-01] [-2.39709631e-01] [-6.62366855e-01] [ 1.93784957e-01] [-1.54019340e+00] [ 4.20618512e-02] [-1.39930766e+00] [ 2.96815032e+00] [ 8.54864204e-01] [-1.11753617e+00] [-3.48083278e-01] [-1.53010714e-01] [-2.50546996e-01] [-4.13107467e-01] [ 1.16914778e+00] [-4.45619561e-01] [-3.26408549e-01] [ 1.14747305e+00] [-1.03083726e+00] [-1.63772968e+00] [-9.76650432e-01] [-5.64830573e-01] [-1.54019340e+00] [ 1.38589508e+00] [ 1.50435498e-01] [-9.98325161e-01] [ 3.88857522e-01] [-1.67024177e+00] [-1.03083726e+00] [ 2.04622322e-01] [ 1.58096764e+00] [ 2.58809145e-01] [ 1.61272863e-01] [-6.51529490e-01] [ 1.11496096e+00] [ 1.47259399e+00] [-1.25842191e+00] [-1.67024177e+00] [ 2.03871217e-02] [ 6.59791640e-01] [-8.89951514e-01] [ 1.93784957e-01] [-4.88969020e-01] [-1.21249724e-02] [-2.28872267e-01] [-8.03252596e-01] [-5.64830573e-01] [-9.76650432e-01] [-1.50768130e+00] [-4.78131655e-01] [ 1.38589508e+00] [ 9.84912581e-01] [ 1.12579832e+00] [-4.46370665e-02] [-2.39709631e-01] [ 1.39598134e-01] [ 2.58809145e-01] [-1.07418671e+00] [ 7.03141099e-01] [-1.15004827e+00] [-5.32318479e-01] [ 4.75556440e-01] [-2.18034902e-01] [-1.34512083e+00] [-5.53993208e-01] [-3.15571184e-01] [ 7.79002651e-01] [-2.28872267e-01] [-8.35764690e-01] [-1.26925928e+00] [-3.37245914e-01] [ 9.54975702e-03] [-3.37245914e-01] [ 2.15459687e-01] [ 2.58809145e-01] [ 2.04622322e-01] [ 2.96815032e+00] [-9.00788879e-01] [-1.17172300e+00] [ 9.09051028e-01] [ 6.59791640e-01] [-6.94878949e-01] [ 2.26372162e+00] [ 2.58809145e-01] [-6.63117960e-02] [-2.93896455e-01] [-5.54744312e-02] [ 2.53465573e+00] [ 3.12244864e-02] [-2.61384361e-01] [ 2.22037216e+00] [ 1.29919616e+00] [-9.54975702e-01] [ 1.82947592e-01] [-1.20498620e-01] [ 1.93784957e-01] [-1.53010714e-01] [ 1.28760769e-01] [ 4.32206981e-01] [ 2.69646510e-01] [-1.41014502e+00] [-3.48083278e-01] [-3.48083278e-01] [-1.42098238e+00] [-9.33300973e-01] [ 4.21369616e-01] [-9.54975702e-01] [ 1.31003352e+00] [-6.84041584e-01] [-9.88238901e-02] [-5.53993208e-01] [-7.59903137e-01] [-1.85522808e-01] [-9.88238901e-02] [-1.07418671e+00] [-4.45619561e-01] [ 1.17923404e-01] [-3.58920643e-01] [-1.07418671e+00] [-1.09661255e-01] [ 6.37365806e-02] [ 7.57327922e-01] [-1.53010714e-01] [ 1.28760769e-01] [-5.97342667e-01] [-6.63117960e-02] [ 1.50510609e+00] [-4.13107467e-01] [-7.71491607e-02] [ 5.28992159e-02] [-2.61384361e-01] [-5.64830573e-01] [ 1.55929291e+00] [ 1.61272863e-01] [ 1.47259399e+00] [-7.49065773e-01] [ 1.03909940e+00] [-9.87487796e-01] [-5.10643749e-01] [-1.00916253e+00] [-1.18256036e+00] [ 2.06864905e+00] [-4.56456926e-01] [-1.03083726e+00] [ 2.58809145e-01] [-1.53010714e-01] [-7.16553679e-01] [ 1.33170825e+00] [ 2.80483875e-01] [ 2.37134416e-01] [-1.20498620e-01] [ 8.54113100e-02] [-4.02270102e-01] [-1.28009664e+00] [-1.74685443e-01] [ 5.29743263e-01] [-5.53993208e-01] [-9.87487796e-01] [ 6.59791640e-01] [-8.46602055e-01] [-3.80595373e-01] [ 6.48954275e-01] [-1.03083726e+00] [-3.91432737e-01] [-1.22590982e+00] [-1.00916253e+00] [-2.29623371e-02] [-2.72221725e-01] [-4.78131655e-01] [ 3.12244864e-02] [-2.18034902e-01] [ 2.58809145e-01] [-1.06334935e+00] [ 1.33170825e+00] [ 1.17923404e-01] [ 2.96815032e+00] [ 6.92303734e-01] [ 8.11514746e-01] [-6.63117960e-02] [-7.59903137e-01] [ 7.45739453e-02] [ 8.11514746e-01] [-9.00788879e-01] [ 2.03871217e-02] [ 2.96815032e+00] [-1.96360172e-01] [-1.76777806e+00] [ 1.33170825e+00] [ 1.06077413e+00] [-2.93896455e-01] [-2.50546996e-01] [-5.21481114e-01] [ 5.28992159e-02] [-2.39709631e-01] [ 6.37365806e-02] [-1.69191650e+00] [-6.29854761e-01] [ 2.61051729e+00] [ 2.96815032e+00] [ 3.12244864e-02] [ 1.39598134e-01] [-1.31335984e-01] [-9.88238901e-02] [-7.81577867e-01] [-7.92415232e-01] [ 5.28992159e-02] [ 1.39598134e-01] [-3.48083278e-01] [-1.16088563e+00] [-5.21481114e-01] [ 9.63237852e-01] [ 1.21249724e+00] [-1.96360172e-01] [-3.04733820e-01] [-2.29623371e-02] [-1.90866380e+00] [ 2.04622322e-01] [-3.48083278e-01] [-8.14089961e-01] [-4.78131655e-01] [-3.58920643e-01] [ 4.86393804e-01] [-2.07197537e-01] [ 1.62431710e+00] [-1.18256036e+00] [ 1.16914778e+00] [ 8.11514746e-01] [-1.31335984e-01] [ 2.46963155e+00] [-2.72221725e-01] [-1.96360172e-01] [ 4.10532251e-01] [-1.32344610e+00] [-8.79865254e-02] [ 1.01742468e+00] [-9.88238901e-02] [-4.56456926e-01] [ 1.65682919e+00] [-5.21481114e-01] [ 5.83930087e-01] [ 6.05604816e-01] [ 1.45091926e+00] [-4.02270102e-01] [-8.24927326e-01] [-1.21249724e-02] [ 8.76538934e-01] [-2.83059090e-01] [-3.80595373e-01] [ 7.45739453e-02] [ 4.32206981e-01] [-1.63848078e-01] [-3.15571184e-01] [-2.83059090e-01] [-1.13921090e+00] [-1.67024177e+00] [-9.11626243e-01] [-5.75667937e-01] [ 5.29743263e-01] [-4.46370665e-02] [-1.26925928e+00] [-3.69758008e-01] [ 1.01742468e+00] [-8.79114149e-01] [ 2.26297051e-01] [-1.08502408e+00] [ 1.50435498e-01] [ 1.61272863e-01] [ 2.96815032e+00] [-7.05716314e-01] [ 2.29623371e+00] [ 4.32206981e-01] [ 1.46175663e+00] [-8.79865254e-02] [ 7.89840016e-01] [ 2.96815032e+00] [ 2.31790844e+00] [-2.18034902e-01] [-3.26408549e-01] [ 6.27279546e-01] [-3.80595373e-01] [-3.37997018e-02] [-1.85522808e-01] [ 6.27279546e-01] [-8.89951514e-01] [ 1.09328623e+00] [-9.54975702e-01] [-1.52935603e+00] [-1.21249724e-02] [ 1.35338298e+00] [ 9.74075216e-01] [-5.21481114e-01] [-7.59903137e-01] [-2.07197537e-01] [ 1.86273912e+00] [-5.21481114e-01] [-3.26408549e-01] [-8.35764690e-01] [-6.63117960e-02] [ 2.83810195e+00] [ 2.58809145e-01] [ 2.80558985e+00] [ 1.39598134e-01] [-2.50546996e-01] [-8.03252596e-01] [-1.31260874e+00] [-3.91432737e-01] [-6.73204220e-01] [-1.49684394e+00] [-6.63117960e-02] [ 2.37134416e-01] [ 2.96815032e+00] [-3.58920643e-01] [ 9.54975702e-03] [ 1.59180500e+00] [ 9.74075216e-01] [-1.55103076e+00] [ 5.28992159e-02] [ 2.96815032e+00] [-9.44138338e-01] [-7.05716314e-01] [ 2.91321239e-01] [-3.37245914e-01] [-1.34512083e+00] [-2.93896455e-01] [ 1.36422035e+00] [-9.98325161e-01] [ 2.26297051e-01] [-1.20423509e+00] [ 8.54113100e-02] [-6.40692126e-01] [-7.81577867e-01] [-4.99806384e-01] [ 6.38116910e-01] [-4.56456926e-01] [ 1.07161150e+00] [ 2.96815032e+00] [ 2.96815032e+00] [-3.26408549e-01] [-5.43155843e-01] [ 2.13367324e+00] [-8.79114149e-01] [-1.01999989e+00] [-6.94878949e-01] [ 7.57327922e-01] [-6.40692126e-01] [-1.04167462e+00] [-3.37997018e-02] [-9.98325161e-01] [-1.90866380e+00] [-3.37245914e-01] [-8.68276785e-01] [-6.63117960e-02] [-1.57270549e+00] [ 2.15459687e-01] [ 1.14747305e+00] [-2.18034902e-01] [-9.22463608e-01] [-9.22463608e-01] [-1.63848078e-01] [ 8.33189475e-01] [ 1.17923404e-01] [-1.31335984e-01] [-4.67294290e-01] [-1.52935603e+00] [ 2.03871217e-02] [ 2.03871217e-02] [-4.46370665e-02] [-9.44138338e-01] [ 2.58809145e-01] [-4.45619561e-01] [-1.69191650e+00] [-1.28760769e-03] [-2.72221725e-01] [ 8.87376299e-01] [ 9.19888393e-01] [ 1.28760769e-01] [-1.09586144e+00] [ 1.07086039e-01] [ 6.37365806e-02] [ 1.72110228e-01] [-4.46370665e-02] [ 5.73092722e-01] [-4.46370665e-02] [ 1.13663569e+00] [-3.58920643e-01] [-4.02270102e-01] [-1.28760769e-03] [ 2.96815032e+00] [ 2.37134416e-01] [-4.45619561e-01] [ 1.49426872e+00] [-3.69758008e-01] [-6.51529490e-01] [ 5.28992159e-02]]
MODEL SELECTION < SELECTIVE BEST ,< AS PER GIVEN DATA >¶
LINEAR REGRESSION MODEL
In [ ]:
from sklearn.linear_model import LinearRegression
LR=LinearRegression()
LR.fit(X_train, Y_train)
Out[ ]:
LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LinearRegression()
Predicting the Test set results < LINEAR REGRESSION >¶
In [ ]:
Y_pred = LR.predict(X_test)
np.set_printoptions(precision=2)
print(np.concatenate((Y_pred.reshape(len(Y_pred),1), Y_test.reshape(len(Y_test),1)),1))
[[24.87 22.6 ] [23.73 50. ] [29.38 23. ] [12.14 8.3 ] [21.44 21.2 ] [19.3 19.9 ] [20.51 20.6 ] [21.38 18.7 ] [18.91 16.1 ] [19.91 18.6 ] [ 5.14 8.8 ] [16.37 17.2 ] [17.08 14.9 ] [ 5.61 10.5 ] [39.98 50. ] [32.5 29. ] [22.47 23. ] [36.85 33.3 ] [30.85 29.4 ] [23.16 21. ] [24.78 23.8 ] [24.68 19.1 ] [20.57 20.4 ] [30.35 29.1 ] [22.44 19.3 ] [10.3 23.1 ] [17.65 19.6 ] [18.29 19.4 ] [35.49 38.7 ] [20.94 18.7 ] [18.3 14.6 ] [17.81 20. ] [20.01 20.5 ] [24.08 20.1 ] [29.09 23.6 ] [19.3 16.8 ] [11.15 5.6 ] [24.59 50. ] [17.58 14.5 ] [15.48 13.3 ] [26.22 23.9 ] [20.87 20. ] [22.31 19.8 ] [15.63 13.8 ] [23. 16.5 ] [25.18 21.6 ] [20.14 20.3 ] [22.91 17. ] [10.05 11.8 ] [24.26 27.5 ] [20.95 15.6 ] [17.33 23.1 ] [24.53 24.3 ] [29.94 42.8 ] [13.43 15.6 ] [21.74 21.7 ] [20.81 17.1 ] [15.52 17.2 ] [14.01 15. ] [22.19 21.7 ] [17.78 18.6 ] [21.59 21. ] [32.9 33.1 ] [31.1 31.5 ] [17.74 20.1 ] [32.75 29.8 ] [18.71 15.2 ] [19.46 15. ] [19. 27.5 ] [22.9 22.6 ] [22.96 20. ] [24.02 21.4 ] [30.75 23.5 ] [28.83 31.2 ] [25.9 23.7 ] [ 5.24 7.4 ] [36.68 48.3 ] [23.77 24.4 ] [27.25 22.6 ] [19.29 18.3 ] [28.63 23.3 ] [19.16 17.1 ] [18.99 27.9 ] [37.78 44.8 ] [39.16 50. ] [23.72 23. ] [25.36 21.4 ] [15.89 10.2 ] [26.11 23.3 ] [16.69 23.2 ] [15.85 18.9 ] [13.09 13.4 ] [24.76 21.9 ] [31.27 24.8 ] [22.17 11.9 ] [20.29 24.3 ] [ 0.6 13.8 ] [25.48 24.7 ] [15.57 14.1 ] [17.98 18.7 ] [25.3 28.1 ] [22.35 19.8 ]]
Evaluating the Model Performance < LINEAR REGRESSION >¶
In [ ]:
from sklearn.metrics import r2_score
# Assuming RMS is the R^2 score
RMS = r2_score(Y_test, Y_pred)
# Format the RMS value with two decimal points
formatted_RMS = "{:.2f}".format(RMS)
# Convert formatted_RMS to float for comparison
formatted_RMS_float = float(formatted_RMS)
if formatted_RMS_float==1:
print(f"The R^2 Score {formatted_RMS} % 'Over Fitted Model'")
elif 0.8 < formatted_RMS_float < 0.9:
print(f"The R^2 Score {formatted_RMS} % 'Perfect Model'")
elif 0.7 < formatted_RMS_float < 0.8:
print(f"The R^2 Score {formatted_RMS} % 'accurate Model'")
elif 0.6 < formatted_RMS_float <0.7:
print(f"The R^2 Score {formatted_RMS} % 'Modrate Model'")
elif 0.5< formatted_RMS_float < 0.6:
print(f"The R^2 Score {formatted_RMS} % 'Under Fitted Model'")
else:
print(f"The R^2 Score {formatted_RMS} % 'Weak Model'")
The R^2 Score 0.59 % 'Under Fitted Model'
SELECTED MODEL < DECISION REGRESSION >¶
In [ ]:
from sklearn.tree import DecisionTreeRegressor
DT= DecisionTreeRegressor(random_state = 0)
DT.fit(X_train, Y_train)
Out[ ]:
DecisionTreeRegressor(random_state=0)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeRegressor(random_state=0)
Predicting the Test set results < DECISION REGRESSION >¶
In [ ]:
Y_pred = DT.predict(X_test)
np.set_printoptions(precision=2)
print(np.concatenate((Y_pred.reshape(len(Y_pred),1), Y_test.reshape(len(Y_test),1)),1))
[[23.7 22.6] [22.4 50. ] [19.2 23. ] [11.7 8.3] [21.7 21.2] [20.3 19.9] [21.2 20.6] [19.6 18.7] [19.9 16.1] [16.1 18.6] [ 7.2 8.8] [17.9 17.2] [14.2 14.9] [ 8.8 10.5] [48.5 50. ] [34.6 29. ] [21.4 23. ] [34.9 33.3] [24.1 29.4] [20.3 21. ] [24.7 23.8] [21.5 19.1] [20.1 20.4] [24.7 29.1] [21.8 19.3] [16.1 23.1] [17.5 19.6] [16.4 19.4] [39.8 38.7] [20.4 18.7] [12.5 14.6] [19.1 20. ] [17.6 20.5] [22.2 20.1] [23.8 23.6] [17.7 16.8] [ 8.5 5.6] [20.3 50. ] [11.7 14.5] [17.9 13.3] [23.4 23.9] [21.1 20. ] [20.9 19.8] [12.5 13.8] [22. 16.5] [22. 21.6] [22.4 20.3] [16.4 17. ] [13.4 11.8] [26.7 27.5] [19. 15.6] [15.6 23.1] [20.3 24.3] [35.2 42.8] [15.6 15.6] [18.9 21.7] [19.4 17.1] [17.5 17.2] [10.9 15. ] [22.5 21.7] [20.6 18.6] [23.1 21. ] [29.6 33.1] [22.8 31.5] [20.6 20.1] [24.1 29.8] [17.4 15.2] [27.1 15. ] [16.3 27.5] [23.1 22.6] [20.6 20. ] [26.4 21.4] [24.1 23.5] [30.5 31.2] [25. 23.7] [ 7.2 7.4] [41.7 48.3] [23.1 24.4] [23.9 22.6] [18.5 18.3] [28.6 23.3] [14.5 17.1] [20.8 27.9] [41.7 44.8] [41.7 50. ] [25. 23. ] [21.2 21.4] [13.8 10.2] [24.4 23.3] [12.7 23.2] [16.6 18.9] [11.8 13.4] [20.6 21.9] [30.1 24.8] [21.1 11.9] [20.9 24.3] [11.9 13.8] [22.9 24.7] [12.7 14.1] [17.6 18.7] [25. 28.1] [21.2 19.8]]
Evaluating the Model Performance < DECISION REGRESSION >¶
In [ ]:
from sklearn.metrics import r2_score
# Assuming RMS is the R^2 score
RMS = r2_score(Y_test, Y_pred)
# Format the RMS value with two decimal points
formatted_RMS = "{:.2f}".format(RMS)
# Convert formatted_RMS to float for comparison
formatted_RMS_float = float(formatted_RMS)
if formatted_RMS_float==1.0:
print(f"The R^2 Score {formatted_RMS} % 'Over Fitted Model'")
elif 0.8 < formatted_RMS_float < 0.9:
print(f"The R^2 Score {formatted_RMS} % 'Perfect Model'")
elif 0.7 < formatted_RMS_float < 0.8:
print(f"The R^2 Score {formatted_RMS} % 'accurate Model'")
elif 0.6 < formatted_RMS_float <0.7:
print(f"The R^2 Score {formatted_RMS} % 'Modrate Model'")
elif 0.0 < formatted_RMS_float < 0.5:
print(f"The R^2 Score {formatted_RMS} % 'Under Fitted Model'")
elif formatted_RMS_float < 0.0:
print(f"The R^2 Score {formatted_RMS} % 'BAD Model'")
else:
print(f"The R^2 Score {formatted_RMS} % 'Weak Model'")
The R^2 Score 0.63 % 'Modrate Model'
SELECTED MODEL < RANDOM FOREST REGRESSION >¶
In [ ]:
from sklearn.ensemble import RandomForestRegressor
RFR = RandomForestRegressor(n_estimators = 5000, random_state = 0)
RFR.fit(X_train, Y_train)
Out[ ]:
RandomForestRegressor(n_estimators=5000, random_state=0)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RandomForestRegressor(n_estimators=5000, random_state=0)
Predicting the Test set results < RANDOM FOREST REGRESSION >¶
In [ ]:
Y_predRFR = RFR.predict(X_test)
np.set_printoptions(precision=2)
print(np.concatenate((Y_predRFR.reshape(len(Y_predRFR),1), Y_test.reshape(len(Y_test),1)),1))
[[24.06 22.6 ] [27.99 50. ] [22.08 23. ] [11.01 8.3 ] [20.77 21.2 ] [20.74 19.9 ] [21.21 20.6 ] [20.14 18.7 ] [20.5 16.1 ] [18.75 18.6 ] [ 8.24 8.8 ] [15.32 17.2 ] [15.02 14.9 ] [ 8.43 10.5 ] [47.33 50. ] [34. 29. ] [21.07 23. ] [34.69 33.3 ] [25.59 29.4 ] [21.19 21. ] [23.72 23.8 ] [21.96 19.1 ] [19.74 20.4 ] [24.5 29.1 ] [20.39 19.3 ] [17.77 23.1 ] [18.65 19.6 ] [16.05 19.4 ] [43.98 38.7 ] [19.2 18.7 ] [14.87 14.6 ] [17.57 20. ] [20.1 20.5 ] [21.47 20.1 ] [22.97 23.6 ] [17.88 16.8 ] [ 8.64 5.6 ] [27.94 50. ] [14.52 14.5 ] [15.51 13.3 ] [22.76 23.9 ] [20.88 20. ] [22.42 19.8 ] [15.16 13.8 ] [23.69 16.5 ] [22.42 21.6 ] [21.15 20.3 ] [16.56 17. ] [14.54 11.8 ] [25.17 27.5 ] [16.32 15.6 ] [19.78 23.1 ] [21.96 24.3 ] [39.87 42.8 ] [15.04 15.6 ] [21.21 21.7 ] [19.68 17.1 ] [19.12 17.2 ] [21.72 15. ] [20.08 21.7 ] [21.59 18.6 ] [21.69 21. ] [33.29 33.1 ] [28.01 31.5 ] [18.36 20.1 ] [26.92 29.8 ] [15.99 15.2 ] [21.17 15. ] [17.28 27.5 ] [22.02 22.6 ] [20.15 20. ] [22.89 21.4 ] [24.37 23.5 ] [30.66 31.2 ] [29.56 23.7 ] [ 8.63 7.4 ] [43.01 48.3 ] [22.41 24.4 ] [22.8 22.6 ] [20.27 18.3 ] [26.57 23.3 ] [18.22 17.1 ] [22.46 27.9 ] [42.5 44.8 ] [41.7 50. ] [24.37 23. ] [22.7 21.4 ] [14.92 10.2 ] [26.44 23.3 ] [16.07 23.2 ] [19.2 18.9 ] [12.04 13.4 ] [22.51 21.9 ] [29.91 24.8 ] [21.13 11.9 ] [22.02 24.3 ] [11.61 13.8 ] [23.42 24.7 ] [14.88 14.1 ] [19.22 18.7 ] [24.05 28.1 ] [20.06 19.8 ]]
Evaluating the Model Performance < RANDOM FOREST REGRESSION >¶
In [ ]:
from sklearn.metrics import r2_score
# Assuming RMS is the R^2 score
RMS = r2_score(Y_test, Y_predRFR)
# Format the RMS value with two decimal points
formatted_RMS = "{:.2f}".format(RMS)
# Convert formatted_RMS to float for comparison
formatted_RMS_float = float(formatted_RMS)
if formatted_RMS_float==1.0:
print(f"The R^2 Score {formatted_RMS} % 'Over Fitted Model'")
elif 0.9 < formatted_RMS_float < 1:
print(f"The R^2 Score {formatted_RMS} % 'Perfect Model'")
elif 0.8 < formatted_RMS_float < 0.9:
print(f"The R^2 Score {formatted_RMS} % 'accurate Model'")
elif 0.7 < formatted_RMS_float <0.8:
print(f"The R^2 Score {formatted_RMS} % 'Modrate Model'")
elif 0.5 < formatted_RMS_float < 0.6:
print(f"The R^2 Score {formatted_RMS} % 'Under Fitted Model'")
elif formatted_RMS_float < 0.0:
print(f"The R^2 Score {formatted_RMS} % 'BAD Model'")
else:
print(f"The R^2 Score {formatted_RMS} % 'Weak Model'")
The R^2 Score 0.77 % 'Modrate Model'
Grid Search CV¶
In [ ]:
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestRegressor
# Define the parameter grid
parameters = {
'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000],
'criterion': ["squared_error", "absolute_error", "friedman_mse", "poisson"],
'max_depth': [20]
}
# Initialize a RandomForestRegressor (you can choose any model)
model = RandomForestRegressor()
# Initialize GridSearchCV with the model, parameter grid, and any other necessary arguments
GCV = GridSearchCV(estimator=model, param_grid=parameters, cv=5) # cv is the number of folds for cross-validation
GCV.fit(X_train,Y_train)
Out[ ]:
GridSearchCV(cv=5, estimator=RandomForestRegressor(),
param_grid={'criterion': ['squared_error', 'absolute_error',
'friedman_mse', 'poisson'],
'max_depth': [20],
'n_estimators': [100, 200, 300, 400, 500, 600, 700,
800, 900, 1000]})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(cv=5, estimator=RandomForestRegressor(),
param_grid={'criterion': ['squared_error', 'absolute_error',
'friedman_mse', 'poisson'],
'max_depth': [20],
'n_estimators': [100, 200, 300, 400, 500, 600, 700,
800, 900, 1000]})RandomForestRegressor()
RandomForestRegressor()
In [ ]:
Best_prarm=GCV.best_params_
print(Best_prarm)
{'criterion': 'squared_error', 'max_depth': 20, 'n_estimators': 100}
In [ ]:
Y_predGCV = GCV.predict(X_test)
np.set_printoptions(precision=2)
print(np.concatenate((Y_predRFR.reshape(len(Y_predRFR),1), Y_test.reshape(len(Y_test),1)),1))
[[24.06 22.6 ] [27.99 50. ] [22.08 23. ] [11.01 8.3 ] [20.77 21.2 ] [20.74 19.9 ] [21.21 20.6 ] [20.14 18.7 ] [20.5 16.1 ] [18.75 18.6 ] [ 8.24 8.8 ] [15.32 17.2 ] [15.02 14.9 ] [ 8.43 10.5 ] [47.33 50. ] [34. 29. ] [21.07 23. ] [34.69 33.3 ] [25.59 29.4 ] [21.19 21. ] [23.72 23.8 ] [21.96 19.1 ] [19.74 20.4 ] [24.5 29.1 ] [20.39 19.3 ] [17.77 23.1 ] [18.65 19.6 ] [16.05 19.4 ] [43.98 38.7 ] [19.2 18.7 ] [14.87 14.6 ] [17.57 20. ] [20.1 20.5 ] [21.47 20.1 ] [22.97 23.6 ] [17.88 16.8 ] [ 8.64 5.6 ] [27.94 50. ] [14.52 14.5 ] [15.51 13.3 ] [22.76 23.9 ] [20.88 20. ] [22.42 19.8 ] [15.16 13.8 ] [23.69 16.5 ] [22.42 21.6 ] [21.15 20.3 ] [16.56 17. ] [14.54 11.8 ] [25.17 27.5 ] [16.32 15.6 ] [19.78 23.1 ] [21.96 24.3 ] [39.87 42.8 ] [15.04 15.6 ] [21.21 21.7 ] [19.68 17.1 ] [19.12 17.2 ] [21.72 15. ] [20.08 21.7 ] [21.59 18.6 ] [21.69 21. ] [33.29 33.1 ] [28.01 31.5 ] [18.36 20.1 ] [26.92 29.8 ] [15.99 15.2 ] [21.17 15. ] [17.28 27.5 ] [22.02 22.6 ] [20.15 20. ] [22.89 21.4 ] [24.37 23.5 ] [30.66 31.2 ] [29.56 23.7 ] [ 8.63 7.4 ] [43.01 48.3 ] [22.41 24.4 ] [22.8 22.6 ] [20.27 18.3 ] [26.57 23.3 ] [18.22 17.1 ] [22.46 27.9 ] [42.5 44.8 ] [41.7 50. ] [24.37 23. ] [22.7 21.4 ] [14.92 10.2 ] [26.44 23.3 ] [16.07 23.2 ] [19.2 18.9 ] [12.04 13.4 ] [22.51 21.9 ] [29.91 24.8 ] [21.13 11.9 ] [22.02 24.3 ] [11.61 13.8 ] [23.42 24.7 ] [14.88 14.1 ] [19.22 18.7 ] [24.05 28.1 ] [20.06 19.8 ]]
Score¶
In [ ]:
G=GCV.best_score_
print(G)
0.8626805578047279
SAVING THE Grid Search CV¶
In [ ]:
from joblib import dump, load
dump(GCV, 'REAL.joblib')
Out[ ]:
['REAL.joblib']
USING THE MODEL¶
In [ ]:
from joblib import dump, load
import numpy as np
GCV = load('REAL.joblib')
features = np.array([[-5.43942006, 4.12628155, -1.6165014, -0.67288841, -1.42262747,
-11.44443979304, -49.31238772, 7.61111401, -26.0016879 , -0.5778192 ,
-0.97491834, 0.41164221, -66.86091034]])
K=GCV.predict(features)
PRICE=(int(K)*1000)
print(K)
[29.55]
C:\Users\surya\AppData\Local\Temp\ipykernel_22704\297953511.py:8: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) PRICE=(int(K)*1000)
[29.55] By GCV¶
[30.12] RFR¶
In [ ]:
print(F"THE PRICE OF THE HOUSE IS {PRICE} $ IN BOSTON CITY")
THE PRICE OF THE HOUSE IS 29000 $ IN BOSTON CITY